Djikstra's algorithm with distance 1 between every node

In summary: GetLength(1); j <= i; ++j) if(source[i,j] != null) // skip this element // accept this element // skip this sub-array
  • #1
SlurrerOfSpeech
141
11
Does anyone know whether there exists a specialized Djikstra's algorithm for when every node has the same distance between it? Or to think of it another way, an algorithm for simply finding the minimum number of moves to get from 1 node to another?

e.g. in the following

Code:
        A  -   B  -  C
        \     /     /  \ 
         D    E    F    G

the shortest path to F from A would be A -> B -> C -> F.
 
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  • #2
Since your graph is effectively unweighted, you can use breadth first search to find the shortest path.
 
  • #3
SlurrerOfSpeech said:
every node has the same distance between it
Between it and what? The next node?
 
  • #4
Can someone help me figure out where I'm going wrong in my solution? It's failing some of the test cases and they're too big for me to possibly step through. The problem is basically that I'm given a matrix like

Code:
.X.X...X
.X*.X.XXX.X
.XX.X.XM...
...XXXX.

and have to move from the 'M' to the '*' along the '.' characters, where in a single move I can travel over any number of characters '.' in a single direction. The 'X' characters are obstructions.

My code is well-annotated. (The only confusing part may be that I transpose the original matrix so that I can access in the more natural [x,y] index structure instead of [y,x]).

Code:
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;

// Algorithm explanation: 
//
// Suppose we have a grid like 
//
//       . . X
//       . . . 
//       * X M 
// 
// Since we can travel along the the dots any distance in a 
// single move, a graph of the possible moves is like 
// 
//          (0,0) -------- (1,0)
//          /   \            |      
//         /     \           |
//       (0,2)---(0,1) --- (1,1)
//                 \        /
//                  \      /
//                   \    /
//                    (2,1)
//                    /                  
//                   /
//                 (2,2)                  
//
// where the distance between every node is 1. We're trying to get
// from node (2,2) to node (0,2) in the shortest route. We can use
// a BFS to find this route.

class Node
{
    // coordinates 
    public int X { get; set; }
    public int Y { get; set; }

    // parent node
    public Node Parent { get; set; } = null;

    // distance from root node
    public int Distance { get; set; } = Int32.MaxValue; // "infinity"

    // nodes connected to this one
    public List<Node> Neighbors { get; set; } = new List<Node>();
}

class ForbiddenForest
{
    private Node[,] _tree;
    private Node _start;
    private Node _end;

    public ForbiddenForest(char[,] mat)
    {
        BuildTree(mat);
    }

    // helper method for traversing a 2-D array
    private static IEnumerable<T> MultiElements<T>(T[,] source)
    {
        for(int i = 0, n = source.GetLength(0); i < n; ++i)
            for(int j = 0, m = source.GetLength(1); j < m; ++j)
                yield return source[i, j];
    }

    private void BuildTree(char[,] mat)
    {

        int m = mat.GetLength(0),
            n = mat.GetLength(1);

        _tree = new Node[m, n];

        // Add all the nodes to the tree with their x-y positions. 
        // Set the start and end nodes when we come across them.
        for(int i = 0; i < m; ++i)
        {
            for(int j = 0; j < n; ++j)
            {
                switch(mat[i, j])
                {
                    case '.':
                        _tree[i, j] = new Node() { X = i, Y = j };
                        break;
                    case 'M':
                        _tree[i, j] = new Node() { X = i, Y = j };
                        _start = _tree[i, j];
                        break;
                    case '*':
                        _tree[i, j] = new Node() { X = i, Y = j };
                        _end = _tree[i, j];
                        break;
                }
            }
        }

        var nodes = MultiElements(_tree).Where(z => z != null);

        // Now add the neighbors. To do this, start at the node's x-y
        // position on the graph and move as far possible up, right, 
        // down and left, collecting the nodes as we move along.
        foreach(var node in nodes)
        {
            int x = node.X, y = node.Y;
            // up: 
            while (--y >= 0 && _tree[x, y] != null)
                node.Neighbors.Add(_tree[x, y]);
            // right:
            y = node.Y;
            while (++x < m && _tree[y, y] != null)
                node.Neighbors.Add(_tree[x, y]);
            // down:
            x = node.X;
            while (++y < n && _tree[x, y] != null)
                node.Neighbors.Add(_tree[x, y]);
            // left:
            y = node.Y;
            while (--x >= 0 && _tree[x, y] != null)
                node.Neighbors.Add(_tree[x, y]);
        }

        // Now fill in the Distance and Parent values by using the BFS
        // algorithm on https://en.wikipedia.org/wiki/Breadth-first_search
        var Q = new Queue<Node>();

        _start.Distance = 0;

        Q.Enqueue(_start);

        while(Q.Count > 0)
        {
            var current = Q.Dequeue();
            foreach(var neighbor in current.Neighbors)
            {
                if(neighbor.Distance == Int32.MaxValue)
                {
                    neighbor.Distance = current.Distance + 1;
                    neighbor.Parent = current;
                    Q.Enqueue(neighbor);
                }
            }
        }    }

    public int OptimalMoveNumbers { get { return _end.Distance; } }
}

class Solution
{   
    static void Main(String[] args)
    {
        int T = Int32.Parse(Console.ReadLine());
        for(int t = 0; t < T; ++t)
        {
           int[] line = Array.ConvertAll(Console.ReadLine().Split(' '), Int32.Parse);
           int N = line[0], M = line[1];
           char[,] matrix = new char[M,N];
           for(int i = 0; i < N; ++i)
           {
                char[] row = Console.ReadLine().Where(c => c != ' ').ToArray();
               for(int j = 0; j < M; ++j)
                    matrix[j,i] = row[j];
           }
           int K = Int32.Parse(Console.ReadLine());
           var ff = new ForbiddenForest(matrix);
           Console.WriteLine(K == ff.OptimalMoveNumbers() ? "Impressed" : "Oops!");
        }
    }
}
 
  • #5
SlurrerOfSpeech said:
Can someone help me figure out where I'm going wrong in my solution? It's failing some of the test cases and they're too big for me to possibly step through.
Then my advice is to try smaller examples, such as your 3 x 3 example you show in comments, or 4 x 4 or 5 x 5.

SlurrerOfSpeech said:
My code is well-annotated.
Debatable. Main() has no comments. Comments in Main() would help the reader understand what it is doing, especially in.the last two lines.
BuildTree could use some comments in the switch statement.
Also, in BuildTree, you have this comment:
// Now add the neighbors. To do this, start at the node's x-y
// position on the graph and move as far possible up, right,
// down and left,
There are eight possible directions from each node, not just the four that you show in the comment.
SlurrerOfSpeech said:
(The only confusing part may be that I transpose the original matrix so that I can access in the more natural [x,y] index structure instead of [y,x]).
IMO, the transpose is not really necessary. The matrix you show as your map example threw me off for a time, because the X in the upper right corner would be in row 0, column 2. IOW, the "natural" indexes of a 2-D matrix are in y, x form. As long as everyone understands your system, it's fine, though.
SlurrerOfSpeech said:
where the distance between every node is 1
Unclear. Better: The distance between each node and an adjacent node is 1.
 

Related to Djikstra's algorithm with distance 1 between every node

1. What is Djikstra's algorithm with distance 1 between every node?

Djikstra's algorithm with distance 1 between every node is a graph search algorithm used to find the shortest path between two nodes in a graph with unit distance between each node. It is named after its inventor, Dutch computer scientist Edsger W. Dijkstra.

2. How does Djikstra's algorithm with distance 1 between every node work?

This algorithm works by maintaining a list of unvisited nodes and their current distance from the starting node. It then selects the node with the smallest distance and visits its neighboring nodes, updating their distances if a shorter path is found. This process is repeated until the destination node is reached, resulting in the shortest path.

3. What is the time complexity of Djikstra's algorithm with distance 1 between every node?

The time complexity of this algorithm is O(|E| + |V| log |V|), where |E| is the number of edges and |V| is the number of vertices in the graph. This makes it a very efficient algorithm for finding shortest paths in graphs with unit distance between nodes.

4. Can Djikstra's algorithm with distance 1 between every node be used for weighted graphs?

No, this algorithm is specifically designed for graphs with unit distance between nodes. For weighted graphs, a modified version of the algorithm, known as Djikstra's algorithm with distance as weights, should be used instead.

5. What are the applications of Djikstra's algorithm with distance 1 between every node?

This algorithm has various applications in computer science, including finding the shortest path between two points in a network, routing in computer networks, and navigation systems. It is also commonly used in GPS devices and map applications to provide efficient route planning.

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